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Archive of posts filed under the Stan category.

Claims about excess road deaths on “4/20” don’t add up

Sam Harper writes: Since you’ve written about similar papers (that recent NRA study in NEJM, the birthday analysis) before and we linked to a few of your posts, I thought you might be interested in this recent blog post we wrote about a similar kind of study claiming that fatal motor vehicle crashes increase by 12% after 4:20pm […]

The network of models and Bayesian workflow, related to generative grammar for statistical models

Ben Holmes writes: I’m a machine learning guy working in fraud prevention, and a member of some biostatistics and clinical statistics research groups at Wright State University in Dayton, Ohio. I just heard your talk “Theoretical Statistics is the Theory of Applied Statistics” on YouTube, and was extremely interested in the idea of a model-space […]

State-space models in Stan

Michael Ziedalski writes: For the past few months I have been delving into Bayesian statistics and have (without hyperbole) finally found statistics intuitive and exciting. Recently I have gone into Bayesian time series methods; however, I have found no libraries to use that can implement those models. Happily, I found Stan because it seemed among […]

Active learning and decision making with varying treatment effects!

In a new paper, Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski write: Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target […]

StanCon 2019: 20–23 August, Cambridge, UK

It’s official. This year’s StanCon is in Cambridge. For details, see StanCon 2019 Home Page What can you expect? There will be two days of tutorials at all levels and two days of invited and submitted talks. The previous three StanCons (NYC 2017, Asilomar 2018, Helsinki 2018) were wonderful experiences for both their content and […]

Some Stan and Bayes short courses!

Robert Grant writes: I have a couple of events coming up that people might be interested in. They are all at Stan Taster Webinar is on 15 May, runs for one hour and is only £15. I’ll demo Stan through R (and maybe PyStan and CmdStan if the interest is there on the day), […]

Mister P for surveys in epidemiology — using Stan!

Jon Zelner points us to this new article in the American Journal of Epidemiology, “Multilevel Regression and Poststratification: A Modelling Approach to Estimating Population Quantities From Highly Selected Survey Samples,” by Marnie Downes, Lyle Gurrin, Dallas English, Jane Pirkis, Dianne Currier, Matthew Spittal, and John Carlin, which begins: Large-scale population health studies face increasing difficulties […]

New golf putting data! And a new golf putting model!

Part 1 Here’s the golf putting data we were using, typed in from Don Berry’s 1996 textbook. The columns are distance in feet from the hole, number of tries, and number of successes: x n y 2 1443 1346 3 694 577 4 455 337 5 353 208 6 272 149 7 256 136 8 […]

stanc3: rewriting the Stan compiler

I’d like to introduce the stanc3 project, a complete rewrite of the Stan 2 compiler in OCaml. Join us! With this rewrite and migration to OCaml, there’s a great opportunity to join us on the ground floor of a new era. Your enthusiasm for or expertise in programming language theory and compiler development can help […]

From the Stan forums: “I’m just very thirsty to learn and this thread has become a fountain of knowledge”

Bob came across the above quote in this thread. More generally, though, I want to recommend the Stan Forums. As you can see from the snapshot below, the topics are varied: The discussions are great, and anyone can jump in. Lots of example code and all sorts of things. Also of interest: the Stan case […]

Data For Progress’s RuPaul-Predict-a-Looza

Data for Progress launched the RuPaul-Predict-a-Looza (and winner), the first ever RuPaul’s Drag Race prediction competition. Statistical models versus NYC Council Speaker Corey Johnson. The prize: bragging rights and the ability to add one policy question on the next Data for Progress survey. First predictions are due this Thursday (February 28). I made a notebook […]

HMC step size: How does it scale with dimension?

A bunch of us were arguing about how the Hamiltonian Monte Carlo step size should scale with dimension, and so Bob did the Bob thing and just ran an experiment on the computer to figure it out. Bob writes: This is for standard normal independent in all dimensions. Note the log scale on the x […]

The Stan Core Roadmap

Here’s the plan for Stan core development that Bob presented at Stancon last week (that is, back at the end of August, 2018): Part I. Rear-View Mirror Stan 2.18 Released Multi-core Processing has Landed! Multi-Process Parallelism Map Function New Built-in Functions Manuals to HTML Improved Effective Sample Size Foreach Loops Data-qualified Arguments Bug Fixes and […]

Stan This Month

So much is going on with Stan that it can be hard to keep track, so we (the Stan project) are starting a monthly update and newsletter.  If you want to be included in the monthly mailing list, just type in your email here. Charles Margossian is the editor of Stan This Month and indeed […]

Principal Stratification on a Latent Variable (fitting a multilevel model using Stan)

Adam Sales points to this article with John Pane on principal stratification on a latent variable, and writes: Besides the fact that the paper uses Stan, and it’s about principal stratification, which you just blogged about, I thought you might like it because of its central methodological contribution. We had been trying to use computer […]

Autodiff! (for the C++ jockeys in the audience)

Here’s a cool thread from Bob Carpenter on the Stan forums, “A new continuation-based autodiff by refactoring,” on the inner workings of Stan. Enjoy. P.S. Just for laffs, see this post from 2010 which is the earliest mention of autodiff I could find on the blog.

Transforming parameters in a simple time-series model; debugging the Jacobian

So. This one is pretty simple. But the general idea could be useful to some of you. So here goes. We were fitting a model with an autocorrelation parameter, rho, which was constrained to be between 0 and 1. The model looks like this: eta_t ~ normal(rho*eta_{t-1}, sigma_res), for t = 2, 3, … T […]

NYC Meetup Thursday: Under the hood: Stan’s library, language, and algorithms

I (Bob, not Andrew!) will be doing a meetup talk this coming Thursday in New York City. Here’s the link with registration and location and time details (summary: pizza unboxing at 6:30 pm in SoHo): Bayesian Data Analysis Meetup: Under the hood: Stan’s library, language, and algorithms After summarizing what Stan does, this talk will […]

Reproducibility and Stan

Aki prepared these slides which cover a series of topics, starting with notebooks, open code, and reproducibility of code in R and Stan; then simulation-based calibration of algorithms; then model averaging and prediction. Lots to think about here: there are many aspects to reproducible analysis and computation in statistics.

Prior distributions for covariance matrices

Someone sent me a question regarding the inverse-Wishart prior distribution for covariance matrix, as it is the default in some software he was using. Inverse-Wishart does not make sense for prior distribution; it has problems because the shape and scale are tangled. See this paper, “Visualizing Distributions of Covariance Matrices,” by Tomoki Tokuda, Ben Goodrich, […]